DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-13 are to a machine and claims 14-20 are to a process.
Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations that fall into the grouping of mental processes and/or mathematical concepts:
1. A system, comprising:
a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer;
a cloud server including a server processor which:
receives the spectrometer data generated by the spectrometer from the processor,
analyzes the spectrometer data,
identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample, compares the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model; determines that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model;
updates the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data; identifies the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model; and provides, based on a plurality of identified scanned samples, a real time heat map visualization of the plurality of identified scanned samples identified as counterfeit and which displays interdiction locations for seizing counterfeit products.
14. (Currently Amended) A method, comprising:
receiving, by a processor, spectrometer data representative of a scanned sample and generated by a spectrometer;
analyzing, by the processor, the spectrometer data;
identifying, by the processor and based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample,
comparing, by the processor, the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model;
determining, by the processor, that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model;
updating, by the processor, the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data; and
identifying, by the processor, the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model; and providing, based on a plurality of identified scanned samples, a real time heat map visualization of the plurality of identified scanned samples identified as counterfeit and which displays interdiction locations for seizing counterfeit products.
The above bolded limitations recite an abstract idea of a mental process, as a person can mentally perform analysis and evaluate received spectrometer data. A person can mentally form a judgement to recognize and identify one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample. The comparison of the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model, at a basic level, amounts to a comparison between two sets of data, which can be performed mentally by a person or by pen and paper to determine whether a match occurs. Depending on the complexity of the machine learning model, the comparing of the one or more unique characteristics of the spectrometer data to one or more unique characteristics for a sample known to a machine learning model could alternatively amount to a mathematical concept to carry out the numerical calculations to calculate a comparison result between two sets of data. The limitation of "determines that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model," amounts to mental process to form an informational-based judgement based on an evaluation between the data characteristics of the scanned sample and the known sample, or can amount to mathematically-based difference comparisons. The limitation of "updates the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data" amount to a mathematical concept to numerically update the values of a machine learning model. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.) Thus, the updating of a model amounts to a mathematical concept to update the variables and values of the model. The limitation of "identifies the scanned sample as one of being counterfeit, not counterfeit, or changed relative to the sample known to the machine learning model," amounts to a a mental process to form a judgement or classification of the scanned sample to identify it as counterfeit, not counterfeit, or changed.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the receiving of spectrometer data representative of a scanned sample and generated by a spectrometer amounts to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)); (2) the system, processor, cloud server including a server processor, and machine learning application amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); and (3) the new limitations of providing, based on a plurality of identified scanned samples, a real time heat map visualization of the plurality of identified scanned samples identified as counterfeit and which displays interdiction locations for seizing counterfeit products, amount to insignificant post-solution outputting activity to the judicial exception (see MPEP 2106.05(g)). Taken as a whole, the independent claims do not provide a further practical application beyond the obtaining of an updated “data”-based machine learning model and an extrasolution display of its calculations, as such abstract information from an abstract data-based model is not further used to carry out any physical application to update, correct, or transform a technology or technical process for an improvement to the technology or technical field.
Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The receiving of spectrometer data generated by the spectrometer does not add something significantly more because similar to above, such limitations amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)), and do not describe any gathering of data using an unconventional measurement arrangement. The elements of a processor, cloud server including a server processor, and machine learning application do not add something significantly more because they amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The new limitations of providing, based based on a plurality of identified scanned samples, a real time heat map visualization of the plurality of identified scanned samples identified as counterfeit and which displays interdiction locations for seizing counterfeit products, do not add significantly more because they amount to insignificant post-solution outputting activity to the judicial exception (see MPEP 2106.05(g)), as tangential activity to the judicial exception.
Dependent claims 2-8, 13, 15-16 contain additional limitations that amount to merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), dependent claims 9-11 and 17-20 describe further mental process data analysis steps that can equivalently be performed by a person that are part of the judicial exception itself, and dependent claim 12 describes further post-solution display activity that is not indicative of integration into a practical application nor something significantly more than the recited judicial exception (see MPEP 2016.05(g)).
3. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensor data, or further physical applications using the updated machine learning model or counterfeit status determination to drive a physical update, physical transformation/change, or physical repair/maintenance of a technology or technical process, could provide integration into a practical application to demonstrate an improvement to the technology or technical field. For example, practical applications (beyond further based data-determinations or extrasolution display) that amount to an integration into a practical application include using the updated machine learning model determined information to carry out physical seizing of determined counterfeit products from entry (see published specification paragraph [0028]) or physical preventing of the sale of counterfeited items (see published specification paragraph [0029]).
Allowable Subject Matter
4. Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: Claim 1 contains allowable subject matter because the closest prior art, Guzman Cardozo (US Pat. Pub. 2019/0310207) fails to anticipate or render obvious a system, comprising: a cloud server including a server processor which: determines that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model; updates the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data, in combination with the rest of the claim limitations as claimed and defined by the Applicant. Claim 14 contains allowable subject matter because the closest prior art, Guzman Cardozo (US Pat. Pub. 2019/0310207) fails to anticipate or render obvious a method, comprising: determining, by the processor, that the scanned sample is or is not counterfeit relative to the sample known to the machine learning model, which includes determining whether or not the scanned sample has been changed relative to the sample known to the machine learning model; updating, by the processor, the machine learning model based on the one or more unique characteristics of the scanned sample identified in the spectrometer data, in combination with the rest of the claim limitations as claimed and defined by the Applicant.5. Dependent claims 2-13 depend from claim 1 and contain allowable subject matter for at least the same reasons as given for claim 1. Dependent claims 15-20 depend from claim 14 and contain allowable subject matter for at least the same reasons as given for claim 14.
Response to Arguments
6. Applicant’s arguments, see Applicant’s Arguments/Remarks, filed August 5, 2026, with respect to the 35 U.S.C. 101 rejections have been fully considered but they are not persuasive.
7. Applicant argues that providing a "real time heat map visualization" according to the recitations of claim 1 is not an abstract idea, but rather a practical application under step 2A prong 2 of the eligibility analysis. Applicant argues that specifically, integrating the claimed "machine learning model" into performing a physical task of providing a "real time heat map visualization" as suggested on page 9 of the previous Office Action is a practical application of the "machine learning model" as set forth in claim 1 (see Applicant's Arguments/Remarks 8/5/2026, pg. 8 paragraph 2).8. In response, the Examiner respectfully disagrees and points out that the new limitations of "providing, based on a plurality of identified scanned samples, a real time heat map visualization of the plurality of identified scanned samples identified as counterfeit and which displays interdiction locations for seizing counterfeit products" amount to insignificant post-solution outputting activity, which does not provide an integration into a practical application in step 2A prong two or significantly more in step 2B (see MPEP 2106.05(g)). The MPEP states that when “whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output)”, the limitations can be mere data gathering or data output (see MPEP 2106.05(g) Insignificant Extra- Solution Activity, in particular item (3)). The results from the analysis/calculations made by the processor must necessarily be presented to a user, and thus the display of the abstract idea analysis/calculation results amounts to a tangential addition to the claim. The analysis of the court in Electronic Power Group is also applicable to the claims in the present case, where the court held that “though lengthy and numerous, the claims do not go beyond requiring the collection, analysis, and display of available information in a particular field, stating those functions in general terms, without limiting them to technical means for performing the functions that are arguably an advance over conventional computer and network technology. The claims, defining a desirable information-based result and not limited to inventive means of achieving the result, fail under § 101," (see Electronic Power Group, LLC v. Alstom, 830 F. 3d 1350, 119 U.S.P.Q. 2d 1739 (Fed. Cir. 2016) at pg. 2). Similarly, the providing of a real time heat map amounts to providing a desirable informational-based display result, but does not "improve" or "advance" any technology, as the informational-based result is merely presented as information and not further used in any way to physically carry out any practical applications for improving a technical process or technology. Therefore, as suggested in the previous office action, the Examiner suggests limitations that use the results of the updated machine learning model to "carry out" or "enable" physical seizing of determined counterfeit products from entry (see published specification paragraph [0028]) or physically preventing of the sale of counterfeited items (see published specification paragraph [0029]). The enacted removal or prevention of sale of identified counterfeit products would amount to a practical application for an improvement to the technical process of stopping the flow of counterfeit items, beyond merely displaying an "informational" based result.
Conclusion
9. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached M to F, 9:30 am to 6 pm.
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PAUL D. LEE
Examiner
Art Unit 2857
/PAUL D LEE/Primary Examiner, Art Unit 2857 8/21/2026
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